{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We award Bitcoins/Ethers: Kaggle with ``Gluon`` and k-fold cross-validation\n",
    "\n",
    "How have you done so far on the journey of  `Deep Learning---the Straight Dope`?\n",
    "\n",
    "> It's time to get your hands dirty and win awards. \n",
    "\n",
    "To encourage your study of deep learning, we will [award bitcoins/ethers](https://www.reddit.com/r/MachineLearning/comments/70gaqs/we_award_bitcoinether_for_users_who_use/) to users who complete this Kaggle competition with ``Gluon`` and beat a baseline score on Kaggle.\n",
    "\n",
    "Let's get started.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this tutorial, we introduce how to use ``Gluon`` for competition on [Kaggle](https://www.kaggle.com). Specifically, we will take the [house price prediction problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) as a case study. \n",
    "\n",
    "We will provide a basic end-to-end pipeline for completing a common Kaggle challenge. We will demonstrate how to use ``pandas`` to pre-process the real-world data sets, such as:\n",
    "\n",
    "* Handling categorical data\n",
    "* Handling missing values\n",
    "* Standardizing numerical features\n",
    "\n",
    "Note that this tutorial only provides a very basic pipeline. We expect you to tweak the following code, such as re-designing models and fine-tuning parameters, to obtain a desirable score on Kaggle.\n",
    "\n",
    "\n",
    "## House Price Prediction Problem on Kaggle\n",
    "\n",
    "[Kaggle](https://www.kaggle.com) is a popular platform for people who love machine learning. To submit the results, please register a [Kaggle](https://www.kaggle.com) account. Please note that, **Kaggle limits the number of daily submissions to 10**.\n",
    "\n",
    "\n",
    "![](../img/kaggle.png)\n",
    "\n",
    "\n",
    "We take the [house price prediction problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) as an example to demonstrate how to complete a Kaggle competition with ``Gluon``. Please learn details of the problem by clicking the [link to the house price prediction problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) before starting.\n",
    "\n",
    "![](../img/house_pricing.png)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Load the data set\n",
    "\n",
    "There are separate training and testing data sets for this competition. Both data sets describe features of every house, such as type of the street, year of building, and basement conditions. Such features can be numeric, categorical, or even missing (`na`). Only the training data set has the sale price of each house, which shall be predicted based on features of the testing data set.\n",
    "\n",
    "The data sets can be downloaded via the [link to problem](https://www.kaggle.com/c/house-prices-advanced-regression-techniques). Specifically, you can directly access the [training data set](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/download/train.csv) and the [testing data set](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/download/test.csv) after logging in Kaggle.\n",
    "\n",
    "We load the data via `pandas`. Please make sure that it is installed (``pip install pandas``)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train = pd.read_csv(\"../data/kaggle/house_pred_train.csv\")\n",
    "test = pd.read_csv(\"../data/kaggle/house_pred_test.csv\")\n",
    "all_X = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'],\n",
    "                      test.loc[:, 'MSSubClass':'SaleCondition']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a look at a few rows of the training data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the shapes of the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing data\n",
    "\n",
    "We can use ``pandas`` to standardiz the numerical features:\n",
    "\n",
    "$$x_i = \\frac{x_i - \\mathbb{E} x_i}{\\text{std}(x_i)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numeric_feas = all_X.dtypes[all_X.dtypes != \"object\"].index\n",
    "all_X[numeric_feas] = all_X[numeric_feas].apply(\n",
    "    lambda x: (x - x.mean()) / (x.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us transform categorical values to numerical ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_X = pd.get_dummies(all_X, dummy_na=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can approximate the missing values by the mean values of the current feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_X = all_X.fillna(all_X.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us convert the formats of the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_train = train.shape[0]\n",
    "\n",
    "X_train = all_X[:num_train].as_matrix()\n",
    "X_test = all_X[num_train:].as_matrix()\n",
    "y_train = train.SalePrice.as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data in `NDArray`\n",
    "\n",
    "To facilitate the interactions with ``Gluon``, we need to load data in the `NDArray` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mxnet import ndarray as nd\n",
    "from mxnet import autograd\n",
    "from mxnet import gluon\n",
    "\n",
    "X_train = nd.array(X_train)\n",
    "y_train = nd.array(y_train)\n",
    "y_train.reshape((num_train, 1))\n",
    "\n",
    "X_test = nd.array(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the loss function to be the squared loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "square_loss = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below defines the root mean square loss between the logarithm of the predicted values and the true values used in the competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_rmse_log(net, X_train, y_train):\n",
    "    num_train = X_train.shape[0]\n",
    "    clipped_preds = nd.clip(net(X_train), 1, float('inf'))\n",
    "    return np.sqrt(2 * nd.sum(square_loss(\n",
    "        nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the model\n",
    "\n",
    "We define a **basic** linear regression model here. This may be modified to achieve better results on Kaggle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_net():\n",
    "    net = gluon.nn.Sequential()\n",
    "    with net.name_scope():\n",
    "        net.add(gluon.nn.Dense(1))\n",
    "    net.initialize()\n",
    "    return net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the training function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def train(net, X_train, y_train, X_test, y_test, epochs, \n",
    "          verbose_epoch, learning_rate, weight_decay):\n",
    "    train_loss = []\n",
    "    if X_test is not None:\n",
    "        test_loss = []\n",
    "    batch_size = 100\n",
    "    dataset_train = gluon.data.ArrayDataset(X_train, y_train)\n",
    "    data_iter_train = gluon.data.DataLoader(\n",
    "        dataset_train, batch_size,shuffle=True)\n",
    "    trainer = gluon.Trainer(net.collect_params(), 'adam',\n",
    "                            {'learning_rate': learning_rate,\n",
    "                             'wd': weight_decay})\n",
    "    net.collect_params().initialize(force_reinit=True)\n",
    "    for epoch in range(epochs):\n",
    "        for data, label in data_iter_train:\n",
    "            with autograd.record():\n",
    "                output = net(data)\n",
    "                loss = square_loss(output, label)\n",
    "            loss.backward()\n",
    "            trainer.step(batch_size)\n",
    "\n",
    "            cur_train_loss = get_rmse_log(net, X_train, y_train)\n",
    "        if epoch > verbose_epoch:\n",
    "            print(\"Epoch %d, train loss: %f\" % (epoch, cur_train_loss))\n",
    "        train_loss.append(cur_train_loss)\n",
    "        if X_test is not None:    \n",
    "            cur_test_loss = get_rmse_log(net, X_test, y_test)\n",
    "            test_loss.append(cur_test_loss)\n",
    "    plt.plot(train_loss)\n",
    "    plt.legend(['train'])\n",
    "    if X_test is not None:\n",
    "        plt.plot(test_loss)\n",
    "        plt.legend(['train','test'])\n",
    "    plt.show()\n",
    "    if X_test is not None:\n",
    "        return cur_train_loss, cur_test_loss\n",
    "    else:\n",
    "        return cur_train_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $K$-Fold Cross-Validation\n",
    "\n",
    "We described the [overfitting problem](../chapter02_supervised-learning/regularization-scratch.ipynb), where we cannot rely on the training loss to infer the testing loss. In fact, when we fine-tune the parameters, we typically rely on $k$-fold cross-validation.\n",
    "\n",
    "> In $k$-fold cross-validation, we divide the training data sets into $k$ subsets, where one set is used for the validation and the remaining $k-1$ subsets are used for training.\n",
    "\n",
    "We care about the average training loss and average testing loss of the $k$ experimental results. Hence, we can define the $k$-fold cross-validation as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,\n",
    "                       learning_rate, weight_decay):\n",
    "    assert k > 1\n",
    "    fold_size = X_train.shape[0] // k\n",
    "    train_loss_sum = 0.0\n",
    "    test_loss_sum = 0.0\n",
    "    for test_i in range(k):\n",
    "        X_val_test = X_train[test_i * fold_size: (test_i + 1) * fold_size, :]\n",
    "        y_val_test = y_train[test_i * fold_size: (test_i + 1) * fold_size]\n",
    "\n",
    "        val_train_defined = False\n",
    "        for i in range(k):\n",
    "            if i != test_i:\n",
    "                X_cur_fold = X_train[i * fold_size: (i + 1) * fold_size, :]\n",
    "                y_cur_fold = y_train[i * fold_size: (i + 1) * fold_size]\n",
    "                if not val_train_defined:\n",
    "                    X_val_train = X_cur_fold\n",
    "                    y_val_train = y_cur_fold\n",
    "                    val_train_defined = True\n",
    "                else:\n",
    "                    X_val_train = nd.concat(X_val_train, X_cur_fold, dim=0)\n",
    "                    y_val_train = nd.concat(y_val_train, y_cur_fold, dim=0)\n",
    "        net = get_net()\n",
    "        train_loss, test_loss = train(\n",
    "            net, X_val_train, y_val_train, X_val_test, y_val_test, \n",
    "            epochs, verbose_epoch, learning_rate, weight_decay)\n",
    "        train_loss_sum += train_loss\n",
    "        print(\"Test loss: %f\" % test_loss)\n",
    "        test_loss_sum += test_loss\n",
    "    return train_loss_sum / k, test_loss_sum / k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and cross-validate the model\n",
    "\n",
    "The following parameters can be fine-tuned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = 5\n",
    "epochs = 100\n",
    "verbose_epoch = 95\n",
    "learning_rate = 5\n",
    "weight_decay = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the above parameters, we can train and cross-validate our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96, train loss: 0.202042\n",
      "Epoch 97, train loss: 0.199900\n",
      "Epoch 98, train loss: 0.197887\n",
      "Epoch 99, train loss: 0.196028\n"
     ]
    },
    {
     "data": {
      "image/png": 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nPQ+FezxOJCIiItVNpCssHFX8zxHF24GeP8TYl4AzgFOBJCATeBKY4pzbFGGe\nGuHo1g35Y9KZDNizlMTCbEKL3sB31DlexxIREZFqJNIVFk5wztnBthLHpRfvyyix73bn3NHOuYbO\nuXjnXFvn3JW1vbgBmBltjp3AFtcAgF1f/sPjRCIiIlLdRHOSXomC0f3b8yonA5C8bT5s+N7jRCIi\nIlKdqLxVM8mJcezodh7B4hOYu776p8eJREREpDpReauGRg4dwEehfgAk/Pg67N7ucSIRERGpLlTe\nqqEj05KZnToGgDiXT+F3z3mcSERERKoLlbdq6qjjR7MylAZA/tdPQijkcSIRERGpDlTeqqnTeqTx\nRuA0AOrlroVVn3qcSERERKoDlbdqKj7gI7HfRPJcAgA7vnjc40QiIiJSHai8VWNjB3fnrdBgAOqt\n/QSy13qcSERERLym8laNtWhYhxVtJwDgI8SeGZq0V0REpLZTeavmTjrhFGaFjgTAP/cpyN3mcSIR\nERHxkspbNTe4Y2Nerxde3zQuuJuimY95nEhERES8pPJWzZkZg04Zy7xQRwBC3zwBe3I8TiUiIiJe\nUXmLASN6t+TFxF8DEF+0k6JZT3icSERERLyi8hYDAn4fR508gcWhtgAEZzwKBbkepxIREREvqLzF\niLF9W/Of+HEAJBRmE/z23x4nEhERES+ovMWI+ICPLieet2/JrMIv/w6FezxOJSIiIlVN5S2GnD2g\nHc8GwmffEvdsIfT98x4nEhERkaqm8hZDEuP8tBoykbWhJgDkf34/BAs9TiUiIiJVSeUtxpwzuBPP\n+McAUCdvA27+NI8TiYiISFVSeYsx9RICpAxOJ9OlAJD/8X26901ERKQWUXmLQROP68I/Kb73LW8D\noVmPe5xIREREqorKWwxqUCeORsddxPJQSwCK/vc3yMvyOJWIiIhUBZW3GHXp0C5MjbsAKF514bM/\ne5xIREREqoLKW4yqmxBgwKnn8HWwGwA251+QtcrjVCIiIlLZVN5i2Ph+rXku+VIA/K6Igul3eRtI\nREREKp3KWwwL+H2MG3EmbwYHAxC/9C34aY7HqURERKQyqbzFuBOPaMonaZeR7wIA7Pnv78E5j1OJ\niIhIZYmovJlZfzN71MwWmVmuma01s5fNrEsZxzc0syfMbEvx+M/MrE8kWWo7M2PSyBN5OjgMgMTM\n2bDkvx6nEhERkcoS6Zm33wFnAZ8A1wJPAEOBuWbW41ADzcwH/Bc4F3gUuBloCnxuZp0jzFOr9WzV\ngIwjLyfZJrorAAAgAElEQVTb1QUg/73fQ+Fuj1OJiIhIZYi0vD0AtHXOXeOc+3/OuXuBIUAAuOUw\nY8cBg4F059wU59xjwAlAEJgSYZ5a76rh/XgkNB6AhJ1rcV/c73EiERERqQwRlTfn3EznXMEB+5YD\ni4AjDzN8HLAJeL3E2C3Ay8AoM0uIJFNt16pREvGDfsOCUDsAQl89CFuWeRtKREREoi5qDyyYmQHN\ngK2HOfRoYK5zLnTA/tlAEnDI++bMrKmZdS+5AR0jzV2TXH1KVx5KvJKQs/DUIW//Vg8viIiI1DDR\nfNr0PKAl8NJhjksDMkvZv3dfi8OMvxJYeMD2Vtlj1lxJ8QEmjB7Nc8FTAIhf9xX88LLHqURERCSa\nolLezKwr8BjwNfDMYQ6vA+SXsn9PifcPZSrQ44BtVJnD1nC/6taM7zpOZotrAEDB+7+H3ds9TiUi\nIiLRUuHyZmbNCT89mgOMc84FDzNkN1DafW2JJd4/KOfcZufcopIbsLK8uWuy340ZyJ/dhQDE79lG\n8CM9ByIiIlJTVKi8mVkD4H2gIXCac25DGYZlEr50eqC9+8ryGXIILRvW4YiTL+TLYHjWFt/cp2Hd\nt96GEhERkaiIuLyZWSLwDuEHDM50zi0u49B5QJ/i+d5KGgjkAXpEMgouOq4DTzW8mnwXh+EoePNq\nKCrtarWIiIjEkkhXWPATfjBhEDDeOff1QY5LM7OuZhZXYverhJ9KHVviuFRgPPCOc04NIwri/D6u\nGncqU4tGAhC/7Ufc53/2OJWIiIhUVKRn3u4HRhK+ZJpiZhNLbiWOuw/4kfBTqHu9CswCnjKzO8zs\nSuBzwA/cGWEeKUXftilsO/oqFhbP/ea+egjWzfY2lIiIiFRIpOXtqOJ/jgCeK2U7qOIHGoYTPnN3\nDfB/hOeGO8k5tzTCPHIQN5/Ziz8nXEu+C+AjROGrl0FBrtexREREJEKRrrBwgnPODraVOC69eF/G\nAeO3O+cudc6lOufqFn/enAr+LlKK5MQ4rvj1SO4vCi+dFZezitBHOsEpIiISq6I5Sa9UU8d2SqVw\nwJXMDh0BgO/bJ2HV596GEhERkYiovNUSN5/enYfr/ZZcF55ir/C1y2F3tsepREREpLxU3mqJOvF+\nrp9wGvcVnQdAXG4mwfdu9jiViIiIlJfKWy3Sp00jGgyZxP+CvQDwL3gJ5k3zOJWIiIiUh8pbLXPt\nKUfwRMoNbHXJAATf+S1sXuJxKhERESkrlbdaJj7g445zTuam0GRCzvAHd1P00gWaPkRERCRGqLzV\nQkc0r8/pI8/lkeAYAALblhJ693pwzuNkIiIicjgqb7XU+H6tWNfzamYEuwPg++FF+P55j1OJiIjI\n4ai81VJmxt1jevH3BjezxTUAIPjfG2DTIo+TiYiIyKGovNViSfEB/nTBydwYuoagM/zBfIpePB/2\n5HgdTURERA5C5a2W69S0PqPHTuDBonEABLavJPTKxRAKepxMRERESqPyJow5uhVZfSbzYbAvAL6V\nH+M+usPjVCIiIlIalTcB4I6RPfl3k1tYEmoNgH39KHz/gsepRERE5EAqbwJAYpyfv6cP5ZaE37PN\n1Qcg9M61sPYbj5OJiIhISSpvsk+z5ETuvvAMrgleT6Hz4wsVUjTtXMhe53U0ERERKabyJvvp1aoh\nE8ZP4PaiiwEI7N5K8D8TIH+Xx8lEREQEVN6kFCN6t6DZ8b/hqaJhAPg3LyT08gUQLPQ4mYiIiKi8\nSamuO6ULs7vcwKfBowDwrfwE9/ZkLaElIiLiMZU3KZXPZ/zt1315rMntzAt1AMDmvwif3O1xMhER\nkdpN5U0Oqm5CgMcvGsodde9gdahZeOdXD8DsJ70NJiIiUoupvMkhNamfwN8vOZVrA39gi0sGwL13\nEyx+2+NkIiIitZPKmxxW+9S63H3RCK4I3UKuS8BwhF69BFZ/4XU0ERGRWkflTcrkqNYNuWrieCYX\nXVc8B1wBoRd+DWtneR1NRESkVlF5kzI78YimDB9zPtcXXkHQGb6iPELPnQXrv/M6moiISK2h8ibl\nMr5fa3qedjE3F14GgK9wF8Fnx0LmDx4nExERqR1U3qTcJg3tSLuTL+XWwksA8OdnE3x2FGz+0eNk\nIiIiNZ/Km0Tk6pM7kzL0MqYUng+Af3cWwadHwJZlHicTERGp2SIub2ZWz8ymmNkHZpZlZs7M0ss4\nNr34+NK25pFmkqp1w6ldCAy+kvsKzwHAn7eF4FPDYdNij5OJiIjUXBU585YK3AEcCcyP8DPuAM4/\nYMuuQCapQmbGrcOPZPeAyfxf4dnA3gJ3BmyY53E6ERGRmqki5S0TSHPOtQVuivAz3nfOPX/AtqcC\nmaSKmRl3jehOVt9r+GPhuQD492QRenoErPvW43QiIiI1T8TlzTmX75zbWNEAZlbfzPwV/Rzxjs9n\n/GlMDwoHTuYPhenhfQU7CD07CjJmeBtORESkhvH6gYXPgB1Anpm9bWadDzfAzJqaWfeSG9Cx0pPK\nIZkZd47oRtJxl3Nz4W8IOcNXmEvo+bGw4hOv44mIiNQYXpW3POBp4CpgDPBX4GRgppm1PszYK4GF\nB2xvVVpSKTMz45bTutLixEklJvLdg3vhbJj/ktfxREREagRPyptz7mXn3EXOuWedc2865/4ADAMa\nA7cdZvhUoMcB26hKDSxlZmZcd0oXug67lCsLryXfBTBXBG9MghkPg3NeRxQREYlpAa8D7OWc+8rM\nvgFOOcxxm4HNJfeZWWVGkwhcfnxHno2/iAvfqc8TcfeTbHnw0R9gZyac+kfweX3FXkREJDZVt79B\n1wEpXoeQ6LhgUDsuOOc8zi26i42uUXjnrKm41y6Bonxvw4mIiMSo6lbeOgBbvA4h0TO8Zxq3XzKO\n8+2PrAi1AMAWvY57djTkZXmcTkREJPZUenkzszQz62pmcSX2NSnluOFAX+CDys4kVeuYDo155PIR\nXJlwH3NCXQCwtTMJPXkSbF3ucToREZHYUqF73sxsMtAQaFG8a4SZtSp+/YhzLge4D7gQaA9kFL83\n08y+B+YAOUAf4GLCl03/VJFMUj11bZ7MU1cN4zf/SuKK7PsZ4Z+Fb/tqQk+ejO/Xz0KHE7yOKCIi\nEhMq+sDCjUDbEj+PLd4AnidczErzEnAGcCqQRHi1hieBKc65TRXMJNVUy4Z1mHbFiVz5Qn1WrnmC\n6wKv48vPwT03Fjvjfuh3kdcRRUREqr0KXTZ1zrVzztlBtoziY9JL/ly873bn3NHOuYbOuXjnXFvn\n3JUqbjVfg6Q4nr54IJv6XM81BVeR7+IwF4R3r4P3b4FgodcRRUREqrXq9sCC1AJxfh9/GtOD3sN/\nw7mFt7HFJYff+OYfuGdHwS49syIiInIwKm/iCTPjkuPaM/mC8ziX+1gUCl99tzUzCP1zKKz/zuOE\nIiIi1ZPKm3jqxK5NefSKUVxX96+8HjwOAN/ODYT+fTrMfc7jdCIiItWPypt47ojm9Xnl6pN4q90d\n3Fl4IUXOhy+YD29Phneug8I9XkcUERGpNlTepFpomBTPvy8aQP2hV3FeQYn74L57CvevU2DbSm8D\nioiIVBMqb1Jt+H3GjcOO4OKJE5nAX/gu1BkA27iA0ONDYMGrHicUERHxnsqbVDvDujfnn1eN5PaG\nf+HxohEA+Apz4bVL4J1roXC3xwlFRES8o/Im1VKnpvV4ffIJrDrqJtILbiLL1Qu/8d3TuCdPgk2L\nvQ0oIiLiEZU3qbbqxPv567jejBqfzlnur3y7d13UzYtxT5wAs/4BoZC3IUVERKqYyptUe2OObsX/\nu3oUdzb6Kw8XjSboDAvmwwe34F4YBzs3eh1RRESkyqi8SUzo2KQer08eytb+N/Hrgj/wk0sFwFZ+\nQmjqIPjxHY8TioiIVA2VN4kZiXF+7h7Vg6suPJ/zAg/8PKnv7ix4aSK8PgnysjxOKSIiUrlU3iTm\nnNi1Ka/99jTe63QXVxdMJsclhd/44SVCjx0DS9/3NJ+IiEhlUnmTmJRaL4EnL+jHoFGXMTJ0P58G\njwLAl7sJpk3QWTgREamxVN4kZpkZ5w5swzPXjuLxlvdxY+Fl7Ch5Fm7qMbD4bW9DioiIRJnKm8S8\ndql1eXHSIHoMv4IRJc/C7doEL58PL54HOzI9TikiIhIdKm9SI/h8Rvqx7Xn2ulH8s+V93FBwOdmu\nbvjNJe8SerQ/zPm35oUTEZGYp/ImNUrbxnWZNmkQR424gpE8xFvBwQD4CnbCu7/FPT0cNv/ocUoR\nEZHIqbxJjePzGecPasdL14/gnU73kF5w08/zwq39Gvf4cfDRHZC/y+OkIiIi5afyJjVWWoM6PHlB\nX84+5xLOjXuIfxedFl6dIVQEM/6Oe2xA+IEG57yOKiIiUmYqb1KjmRnDe6bxzvWnsbzPbYws+CNz\nQ53C7+1YH36g4YVxsG2lx0lFRETKRuVNaoUGSXHcN7YX91xxLnc0foDfFf6G7a5e+M0VH+MeGwjT\nb4M9Od4GFREROQyVN6lV+rRpxFtXD6XbGZMZaQ/xYtEJhJxhoUL4+lFCD/eB756BUNDrqCIiIqVS\neZNax+8zLhzcjtdvGMnsnlMYVXAPc0JdAPDlbYV3rsE9cTys/tLjpCIiIr+k8ia1VpP6CTzw66OY\ncsVE7mn6IFcXTGaDSwHANi6AZ86E/0yALUs9TioiIvIzlTep9fq0acQbVx7LieOuYEL8ozxUNJbd\nLj785rL3cVMHwbu/hV2bvQ0qIiKCypsIEJ4bbmyfVnxw0zBCQ29hWOghXtp7P5wLwpx/E/r7UfD5\nnyF/p9dxRUSkFou4vJlZPTObYmYfmFmWmTkzSy/H+IZm9oSZbTGzXDP7zMz6RJpHJBqS4gNcf+oR\nvHTjWL7tfTfDC+/j82BvAHyFufD5fbiHesPXU6Fwj8dpRUSkNqrImbdU4A7gSGB+eQaamQ/4L3Au\n8ChwM9AU+NzMOlcgk0hUpDWow9/G9+bBq8/j3+3/xsSC37Mw1A4A270Npv8e90gfmPscBIu8DSsi\nIrVKRcpbJpDmnGsL3FTOseOAwUC6c26Kc+4x4AQgCEypQCaRqDoyLZlnLx7AZRddwi2NH+aqgmtY\nGUoDiif5fXtyeI64H17R9CIiIlIlIi5vzrl859zGCIePAzYBr5f4vC3Ay8AoM0uINJdIZRjSuQlv\nXz2U4ROu5LLkx/hd4W9+fjI1awW8fmn4wYaFr0Eo5HFaERGpybx6YOFoYK5z7sC/5WYDSUCXgw00\ns6Zm1r3kBnSsxKwiQPihhjN6pfHBb0+k7+hrOTdhKvcUTmSLSwbAti6FVy/GPX4sLHpDJU5ERCqF\nV+UtjfBl1wPt3dfiEGOvBBYesL0V1XQihxDw+zi7f2um33wqrYffyNi4x7m38Dy27i1xmxfDK+m4\nqcfA/Bd1T5yIiESVV+WtDpBfyv49Jd4/mKlAjwO2UVFNJ1IGCQE/6ce256PfnUaL029idOAf3Fd4\nDlnFa6ba1qXwxmXhBxvmPAVFpf1PXkREpHy8Km+7gdLua0ss8X6pnHObnXOLSm7AysoIKVIWiXF+\nLj6uPR/97nSanHYzowL/4J7C89jsGgJg2Wvg3evCU4zMeBj27PA4sYiIxDKvylsm4UunB9q7b0MV\nZhGJijrxfi4d0oEPf3cGrYffxPiEx7m98CJ+cqkA2K5M+OgPhB7sDh/fBTs3eRtYRERiklflbR7Q\np3i+t5IGAnnAsqqPJBIddeKLL6fePIyeo6/nwrqPc2PhZSwPtQTAl78DvnoQ92APePsarZ0qIiLl\nUunlzczSzKyrmcWV2P0q0AwYW+K4VGA88I5zTjcHScyLD/j4df82TL/hZIaefR2/bfwPLim4gW9D\n4YepLVQAc5+BxwbAC+Nh5WfgnMepRUSkugtUZLCZTQYa8vPToSPMrFXx60eccznAfcCFQHsgo/i9\nV4FZwFNm1g3YSvgpUj9wZ0UyiVQ3Ab+Pkb1bMKJXGjNWdOfhL4aRt2IGlwXe5RTfXHzmYPmHsPxD\nXNNu2KDJ0OMsiEs8/IeLiEitY64C/6VvZhlA24O83d45l2FmT1Nc3pxzGSXGNgL+DxhN+OnSb4Eb\nnXNzIsjRHVi4cOFCunfvXt7hIlVu4focnvxyFQt/mMuFvvcZ7/8fdaxg3/uhpFR8fdOh/yWQfKiZ\nc0REJNYsWrSIHj16APQofvCyXCpU3qoLlTeJVZk5u3lm5hre/WYRIwunc2FgOs0se9/7zvxYt5Ew\n4DJocwyYeZhWRESiQeUNlTeJfbn5Rbw29yee/XIZ3bI/Jz0wnT6+Ffsd45r1wPpfCj3HQ0I9j5KK\niEhFqbyh8iY1Ryjk+N+yLTw9M4Pty2dxYWA6Z/pmkWA/r9IQiq+P76hzoN8l0LSrh2lFRCQSFS1v\nFXpgQUSiy+czTuzalBO7NmX11u48+/XJPDJnIcMLP+a8wCe0tG34CnbC7Cdg9hO4toOxPunQbSTE\nHWphEhERqSl05k2kmsvNL+LNeet5YeYqWmz5kvP9H3G8/4f9jgklNsTXewL0uRCadfMoqYiIlIUu\nm6LyJrWDc465a7fz/Ky1LPjhe8bZx4zzf0Gq7b/clmvZDzt6IvQYC4kNPEorIiIHo8umIrWEmdG3\nbQp926aQdWY3XvvueM6dvZJOWV8wwf8ZQ/0LwsetnwPr5xD64BZ83UZDn/Oh7bF6UlVEpIbQmTeR\nGOac49uM7bz47Vrm/zCP0XzGWf4vaGFZ+x0Xatg2fFm116+hcUeP0oqICOiyKaDyJgKQs7uQt+et\n57U5a0jOnMHZ/v/xK9+c/Z5UBXCtBmC9J0D3MZCU4lFaEZHaS+UNlTeRAy3ZuINX5vzEZ3OXMCT/\nc8b4v+Qo36r9jnG+OKzzqdBrPHQ5TU+riohUEZU3VN5EDqagKMRnSzfzxtz1rFnyPWfa/xjj/+qX\nl1Xj6+PrNgp6joN2Q8Cv22FFRCqLHlgQkYOKD/gY1r05w7o3JzuvJ+/+8CuumbuOuJ9mMto3g9P9\n35Bsu8Nzx817HuY9H15Xtfto6D4W2gwCn8/rX0NERErQmTeRWmjttjzemree/36/mvZZXzHaP4MT\nfPN+cX9cqF4avh5joNtoaNVfRU5EJAp02RSVN5FIOedYnLmDt+Zt4LPvl3FU3leM8H3NYN8iAhba\n79hQvbTiM3KjodUAFTkRkQipvKHyJhINoZDju7XbeXf+Bmb8sJSBe77iTN8sBvp+xGf7//9EqF4a\nvm4j4MgR0Gaw7pETESkHlTdU3kSiLRhyfLN6G+/+kMmcBT8yIH8mw33fMND3I/4DilwwMQX/kcPh\nyJHQ/niIS/QotYhIbFB5Q+VNpDIVBUPMzsji/QUb+WbBEvrvmcFpvtkM8i3+5aXVQBLW+WTsiDOg\nyzDNIyciUgqVN1TeRKpKMOSYk5HFB4s28vXCFXTfOZPT/LMZ6ltAghXud6wzP7Q5BjtieHgeudRO\nHqUWEaleVN5QeRPxgnOOhet3MH3RRr5YuIqW277mVP8cTvJ9TwPL+8XxwUYd8B9xeviMXJtBEIj3\nILWIiPdU3lB5E6kOVm/N5ePFm/h08Xp8a7/mFN8cTvHNpbVvyy+ODcbVw9fppPAKD51/BfWbe5BY\nRMQbKm+ovIlUN9tzC/hs6WY+XryR9cvnc0zRt5zk/55+tvQXDzwABJv1xN9lGHQ6OTyfnD/Og9Qi\nIlVD5Q2VN5HqrKAoxJyMLD5dsplvl6ykbdbXnOifx/G++aTYrl8cH4yrj6/j8VinU8JlrmEbD1KL\niFQelTdU3kRiyZptuXy+dAufL8kkd9W3HMtcTvJ9T09fRqnHFzXsQKDzSdDhRGg/BBIbVG1gEZEo\nU3lD5U0kVu0pDPL1qm38b+kWfliynDbZ33C8fz5DfAtItR2/OD5kflyLPvg7nggdjg9fYg0keJBc\nRCRyKm+ovInUFOuy8vhi+Ra+XLqJ7Su/o0/RPIb4fqCvb9kv1l0FCPrrQNtB+DueAO2GQFpv8Pmr\nPriISDmovKHyJlITFQVDzP8phxkrtvLt0p+IW/81g+0HjvUt4kjf2tLHxNXH2g3G335o+BJrsx4q\ncyJS7VS0vGlBQhGplgJ+H33bNqJv20Zwcmdy84fwzeptvLZiG4uWryR1yywG+xZxrG8hbYqnIwkU\n7oTl08MbxWWu7SD87Y+DtseGz8zpSVYRiXEqbyISE+omBDipazNO6toM6EZW7jBmrdrGP1dsZfWK\nxbTMnsMxvh85xreYlrYNKC5zKz4Mb0AwkIRr1Z9Au2Oh7SBo2Q/ikzz8rUREyi/i8mZmCcDdwPlA\nI+AH4Hbn3EeHGZcOPHWQt9OccxsjzSQitUdK3XiG90xjeM80oCebd47im1VZTF25lTUrF9Ei+zsG\n+pYw0PcjrWwrAP6iPMj4X3gDQhZHUfNexLcbBG2OgdbHQL0mHv5WIiKHV5Ezb08D44CHgOVAOvCe\nmZ3onPuqDOPvAFYfsC+7AnlEpBZrWj+REb1bMKJ3C6AXW3aO5duMLP7f6iwyViyh8bbZ9Lcl9Pct\npaMvEwCfKyQ+8zvI/A6+fhSA/OR2xLUbhK/NQGg9AJp01X1zIlKtRPTAgpkNAL4BbnLO/a14XyKw\nENjsnBt8iLHphM+89XfOzYkkdCmfqQcWROSQcnYXMnfNdr7NyGL5ylUkbJxNH/cj/XxL6WZrCFio\n1HFFgbqEWvQlvv2g8NQkLftCUkoVpxeRmsSrBxbGAUHgib07nHN7zOxfwJ/MrLVzbt3hPsTM6gN5\nzrlghDlERMqkQZ04TuzalBO7NgW6kl80jAU/5TAjYzv/zNhA0do5dM5fRF/fMvr6lpNseQAEinJh\n7Rfhrdie5PbEtR2Av3V/aNUPmnaHQLxHv5mI1DaRlrejgWXOuQNn0Zxd/M+jgMOVt8+AekCBmU0H\nbnDOLT/cF5tZU+DAm1I6Hj6yiMjPEgJ++rVLoV+7FKAjzh3Hmm15fLdmO39Zs42tGQtptO17jrbl\n9PEtp7Nv/b6xiTtWw4LVsOAlAIK+eAqa9CChbT98LftByz6Q0hF8Po9+OxGpySItb2lAZin79+5r\ncYixeYTvl/sM2AH0Ba4HZppZnzKcsbsSuLNcaUVEDsPMaJdal3apdTmrbyugN7n5v+aHn3L4aN12\nHlm1jtBPc+iQv4Sjfcs52reChpYLgD9UQJ1Nc2HTXPZekCgM1KPw/7d377GRXud9x7/P3G8c3u/i\nXrX0rrSWJUWW6sQtbNhOU1uAnUZu0xT1JW7k2EhzKewgUFAHrosIbgrUtdU0cOsmbd0mlhS1tuM4\nLVrXFhIrchNdrJUsrbhaaskllxzehpzh3Of0j/fl7ixFcknuckgufx/gxXDOe87MmXl2dp5533PO\n2/NmYofvITB4F/TfCR3HwGz3XqSI3BS2O+btHPCKc+69q8qPAeeAX3POfWELj/d24Engy865X7xG\n3fWOvH1dY95EZCc555jIFnnuwgLPXZhjavRFYtPPc6r+Km8JnON2e52oVdZtXw61UOk5TezQ3QQH\n7vLWnes8rgkRIgfMbo15KwBrXVAw1rB/05xzf25mTwPv3kTdaWC6scz0S1ZEmsDMGGyLM9gW5313\n9AO3U609wEgmxw/Hs3z9QobFC8+Tmj3Dbe4cdwReY9jGCZs3rDdSXSIy8RRMPHX5MSvBOKWOU0SH\n7iQ8eCf03wHdpyAcW6cXInLQbTd5mwQG1yjv928ntvGYY8CbttkfEZFdEQoGONmX5mRfGu4ZAu6m\nXK1zdmqJFy5meXRsmuULz5GYe5GT7jxvDpxn2MaI+AlduFYgnHkGMs/AM95j1gmynD5GoP808aG7\nsL7T0PdmSPXs3gsVkT1ju8nbc8A7zSy9atLCfQ37t+oYkNlmf0RE9oxIKMDpwVZOD7bCvYeAe6jU\n6pzL5Hjx4iL/YzzD0oUXiM2c4XjtNW4PjHLKLpCwEgABaqQWX4XFV+GV/375cQuRDiqdp4gOvYXo\nwB3Qext0DUM4vkuvVER2w3aTt8eBTwEPAivrvEWBjwJPr0w6MLN+oBU455yr+GXdzrmrkjQzey/e\nxIUvbrM/IiJ7WrjxCN2P3QLcRb3uGJ8v8NJklicvzjN34SUCU2foK45wm73OqcAFeuzK2uXx8hzx\nyb+Ayb+4XFYnQD55iHr3KRK3nCbcf7t32rXzuK7jKnKT2lby5px72sweAx72JxCMAB8GjgAfa6j6\nsF9+FBj1y75vZs8CfwVkgbuBn8c7bfrb2+mPiMh+FAgYhzoTHOpM8FOn+4HbgAfIFiq8cmmJb08u\nMj42SmXiBZLzL3PMjXLKxrjVxi+fdg1QpyU/CvlRGP325ceuWYhc6giu6yTxW04T7TvlXS2i45jW\npBPZ567n8lgfAj7H1dc2vd859+SGreBrwPuAnwQSeOPn/j3wWefc1HX0R0TkptAaD3Pv0Q7uPdqB\n95v4HdTrjrH5ZV6+tMR3JuZZGHsJy7xEW26EYcYYtjEOB67M5Qq6Kq1LI7A0Auf/5HJ5jSBLycPU\nOoeJ9p8kOXAK634TdJ6AaKrpr1VEtm5bS4XsNbo8logcVOVqndHZPGenljg/Mc3yxRcJZF6hLX+O\nWxljODDOoM1u6rGWIr0UW48S6B6mZfAkkd6T0HUC0rdowWGRG2i3lgoREZE9IBIKMNzbwnBvC9wx\ngHeBG6jU6rw+m+f5qRzfnLhE7uKPsJmzpHMjHHPjHLcJhmyaoF35Ad9SnqIlMwWZv4SXrjxHxSIs\nJg5RbTtGqGeY9OCbCHef8MbVJbu18LBIkyl5ExG5CYWDAW7taeHWnhZ4cz/eVQ2hXndcXChwLpPj\ne5fmWJx4mXrmLPHsOXrKFzhuExyzSZL+zFeAsCvTmR+B/Ahc/F/w7JXnKQaSLCYPUW09Srj7OKn+\nYeK9t0L7UWjpU2InsgOUvImIHCCBgDHUkWCoIwFv6gFOXt6XL1U5P5PnO5kc0xOjFC+9QmBuhFRu\nlFIq9wgAABGJSURBVIHaBMdsgiHLELL65Taxep7Y0o9g6UcwzlWJXdmiZGODlFoOYR1HiPfcSnrg\nBKHOY9B2SAsRi2yTkjcREQEgGQ1dWZ/uzkHgJy7vm8+XOT+b5/nMPPPjI1QyrxKcP09L/nX6axMc\nDVyin9mrTsNGXInuwmtQeM27Ls7LVz9fNtRFLj5IJX2IYMcR4j1Hae2/lXDnYW+cXVBfUSJr0SdD\nRESuqT0ZoT0Z4e5D7fBjx/AWDPBklyu8Ppfn+ZkFFibOUZkewRZGSSy9TkdlkiGmOGTTxFZd97W1\nOkPr0gwsPQ8Xr36+GgGyoW5y8QEqLYNY22Fi3UdI9x4j2XMYa71FixPLgaXkTURErktrIswdiTbu\nuKUN7jwCvOvyvmqtzmS2yDOzOTKXLrB8aYT63CihxTFShXG6q5cYsml6mSfQcNQuSJ2O6hQdS1Ow\n9OyaF11cDLSyGOmlmOij1jJIsG2IWOch0r1HaOk9jLX0a6FiuSkpeRMRkR0TCgaujLE70QPcc9X+\nUrXGxEKRp2YWWLh0nsL0eerzrxNZukCyMEl7ZYoBy9C3KrkDSNezpItZKJ6FOeD1q5+7RoBsoJ3F\nSDeFWB+1VD+B1gEi7YMkuw/T3nuYWKeO4Mn+o+RNRER2TTQU5GhXkqNdSTg5CLz9qv21umMmV+L5\n2Sxzk6MsZ0apzo8RWBwntjxJunSJrlqGfpslZcWr2gap01GfpaM4C8WXYQFvUsUqi6TIhjrJR7op\nxXupp3oJpPuJtA2Q6ByktWeIdNcggYiSPNkblLyJiMieFQwYvekYvekYHO0F7ntDnUqtTmaxyEhm\nmsWpUQqzF6jNj2G5S8SWL5EqTdFRy9DL3BsSPIA0OdLVHFRfh2VgnTWNs6TIBtpZCndSinZRSXRD\nqo9gupdoWx+JjgHSXYO0dfURDunrVXaO/nWJiMi+Fg4GGGhPMNB+BIaPrFnHOcfCcoVXZ2fITo2S\nnxmjunARFicI5aeIFadpqc7QUZuji4WrlkNZ0UqO1noOSmNQAhbX7k/VBZixFrKBdnKhdoqRDsrR\nDuqJLizVTSjVQ7Stl0RHHy3t/bS1tZGMhjCtiSebpORNRERuembmz5gdgEMD69ZzzrG4XGI+M8Fi\nZpzC3EUqCxO4pUsE89NESjMkyzOkq/N0urk3zKAFCFmdLrJ01bNQHoUykGPdI3pFF2aSNFlrJR9q\noxBqpRxtpxbroB7vJJDsJNzSRSTdTTzdTbK9h7aWJK3xMLFw8Ia8P7K/KHkTERHxmRmtyRityWNw\n5NiGdV29TnZxgWxmjPzMBIWFSarZSVwuQ7AwQ6Q4S6w8T6o2T1t9gQRvPGULELMKA8wywCxU8bYi\nkF3/uRddnIxLkbUWcoEWCsE0xXAblUgr9Wgr9VgbgUQ7wWQH4WQH0ZYOYukuWlIp0rEwLbEQLbEQ\noaCuWbsfKXkTERHZBgsEaG3roLWtA0685Zr1XSnH0twUS3OTFOYnKS5MUc/N4PIzBAqzhIuzRMsL\nJGpZUrUsSQrrPlbaCqStwBAZcFxJ+tZvAkDBRciSZNolGSFBzlIUAilKoRYq4Raq4TS1aBoXTWOx\nVgLxNkKJVsLJNqLJdhLJBKloiGQk5N1GgySjIaKhgE77NpGSNxERkSawaIp0f4p0//HNNaiWcMuz\nLM9PsZzNUFiYppKbpbo0g1uewQrzBEsLRMoLxCpZErVFEi5PALfuQ8atTJwyfTZ/pdABFX+7hpIL\ns0icnIuzQIJxFydHnDwJisEE5WCScjBJLZyiFk7iIi0QSWGxFIFommAsRTCeJhxvIRaNkIyESESC\nxCNB4uHg5b8TkRDxcJBYWEnhWpS8iYiI7EWhKJYeIJkeILnZNvUaFLNQXKCen6ewOENhaZZybo5q\nbo7a8jwU5rDiIsHyIqHKIpHKEvHaErH6xokfQNQqdFOh29aZrVHzt/K1u7rsouSJkndxlomRJ8qC\ni5En5t13UQrEKAfiVIJxqsEEtXCceiiOC8ZxkSQuFMciCSySIOBv4UicWCRILBwkGg4SCwUu38bC\nQaL+/ah/PxIKeGWhAJFQgEhw7yeMSt5ERERuFoEgJDog0UGgA5KwhcSvDuUlKC76CWCWemGBUn6B\ncn6eSj5LrZClXshCaRFKOQLlJYKVHOFKjnAtT7SWJ0htU0+XsBIJSusngo0aTw1fQ80ZBaIUiFB0\n3m2BKEUiFF2EBSIUiVBw3m2RCCUilFzY/ztMNRDlNz71EO2t6U29lmZT8iYiIiIQCECs1dsY8oqA\nuL9tinNQLUIp5yV45RyUlrz75RyutES1sES5sEitsES9lKNeXIJyHso5rLJMoJInWF0mVF0mVCsQ\ncps4jNcgaI4URVIU4ToOoOX59PYb7zAlbyIiInJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d2f6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.188780\n",
      "Epoch 96, train loss: 0.197970\n",
      "Epoch 97, train loss: 0.195756\n",
      "Epoch 98, train loss: 0.193758\n",
      "Epoch 99, train loss: 0.191826\n"
     ]
    },
    {
     "data": {
      "image/png": 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OyluYShNaApAa3BFaIktEREQEfjRhcKSpvIXJJbUCIJ5SgkX5HqcREZHGzswoKyur9eIg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\nV69z3wEz+4SZzZhZwsz+3cxeuNmxiIiIiOwUV3P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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11441c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.211493\n",
      "Epoch 96, train loss: 0.199204\n",
      "Epoch 97, train loss: 0.197026\n",
      "Epoch 98, train loss: 0.195006\n",
      "Epoch 99, train loss: 0.193052\n"
     ]
    },
    {
     "data": {
      "image/png": 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zi8zsH8BMQpdN/xRmniah27BT2OjSASj85B8epxEREZHGKNynTQdW/HlKxban\np/Yx9jngJOB4IAlYDzwKTHXObQgzT5NwXP8OPP7ysVzCC6TlfRtaLqv9YK9jiYiISCMS7goLRznn\nbG9bleMmVexbVWXfTc65Qc65dOdcnHOus3PukuZe3ACS42PY1vccyl3or2X7xw97nEhEREQam0hO\n0isRMPbIQ3g7GFrfNP67l6Aoz+NEIiIi0piovDUyvdul8EWb0wGIdSWUffWEx4lERESkMVF5a4SG\nHDmGZcFsAEo+fRSCQY8TiYiISGOh8tYIHd8vi//GhKYNaVH0Ayx7x+NEIiIi0liovDVCcTE+EodO\nrFxxoeCjv3ucSERERBoLlbdGavzhfXkpOAKAFmtmwZZV3gYSERGRRkHlrZFql5bAii4TAPDhKJ79\nqMeJREREpDFQeWvEjj3qaD4P9gn98M2TULbD20AiIiLiOZW3Ruywbq15Ozm0gEVCeT7B+TM8TiQi\nIiJeU3lrxMyMLkecxYaK9U53vH+vpg0RERFp5lTeGrlTh3ThcXcyAMkFK2DRyx4nEhERES+pvDVy\nKQmx+Ib+kjzXAoDCd+8E5zxOJSIiIl5ReYsC5x3Vj8eDowFIzlsIS//ncSIRERHxispbFMhMSaB4\n4C8pcIkAFL5zh86+iYiINFMqb1HivGMH8lRwFADJG7+BlR96nEhERES8oPIWJdqnJ7Kp3y8pqlgy\nq/DdOz1OJCIiIl5QeYsi5x13MNMCxwCQvPYTWPOFx4lERESkoam8RZGuGcms7H0BJS4GgKJ3dPZN\nRESkuVF5izLn/uxQng8cCUDS6ndg/TyPE4mIiEhDUnmLMr3bpZDT9ReUu9Bf3Y63/+hxIhEREWlI\nKm9R6OxRI3ghMBKAxBVvwerZHicSERGRhqLyFoUO7JDOZ50vYoeLA6D4tRs075uIiEgzofIWpSaf\nfAT/CoRWXUjY+A3k/NfjRCIiItIQVN6i1AFZqazvfxG5LhWA4jdvgfISj1OJiIhIfVN5i2JTThzE\ng8HTAUjY/j3uy395nEhERETqm8pbFMtKSyT5sAtZEWwHQNmsP8OOrR6nEhERkfqk8hblJh/Tm4di\nJgIQV5pP4KN7PU4kIiIi9Sms8mZmQ83sATPLMbNCM/vezKabWa8ajk83s0fMbFPF+FlmNjicLM1d\nakIsA46dyJxg6B+9++zvsHWNx6lERESkvoR75u064HTgXeAK4BFgJPC1mfXf10Az8wGvAT8HHgCu\nBTKB98086zB5AAAgAElEQVSsZ5h5mrWzh3XmsaRfAhATLKX0f7d4nEhERETqS7jl7V6gs3Pucufc\nP51ztwMjgBjg+v2MHQ8cDkxyzk11zj0IHAUEgKlh5mnW4mJ8nHzSWF4PHBL6eeELsOIDj1OJiIhI\nfQirvDnnZjvnSvfYtxTIAQ7Yz/DxwAbgxSpjNwHTgVPNLD6cTM3dCf3b8VLmJRS50D++0plXauoQ\nERGRJihiDyyYmQFtgdz9HDoI+No5F9xj/xdAElCj++Zkd2bGZacdw18CoalD4vJX4D6+z+NUIiIi\nEmmRfNr0HKA98Nx+jssC1lezf+e+7H0NNrNMM+tXdQO61zptEzSgQxrlQy9mUbATAMEP74HNyz1O\nJSIiIpEUkfJmZn2AB4FPgcf3c3giUN31vOIq7+/LJcCCPbaZNQ7bxP1mVF/ujruYoDP8wVLKXr5K\n656KiIg0IXUub2bWjtDTo/nAeOdcYD9DdgDV3deWUOX9fXkI6L/HdmqNAzdxKQmxnH7qOKYFjgEg\ndvX7sOAFb0OJiIhIxNSpvJlZGvAGkA6c4JxbV4Nh6wldOt3Tzn37/Azn3EbnXE7VDdC1wSpO7N+O\nT7pcWrnuaelr12nlBRERkSYi7PJmZgnAK4QeMDjZObewhkPnAoMr5nurahhQBCwJN5OEmBm/G3cY\ndwbPBSCuOJfA27d6G0pEREQiItwVFvyEHkw4DDjDOffpXo7LMrM+ZhZbZfcMQk+ljqtyXAZwBvCK\nc07zW0RAx1ZJdDn6F3wS6AeA/+vHYPksj1OJiIhIXYV75u0eYAyhS6atzGxi1a3KcXcAiwg9hbrT\nDOAz4DEzu9nMLgHeB/yAlgaIoF+N7M4/0i6nsGLut7IXf63LpyIiIlEu3PI2sOLPU4Anq9n2quKB\nhtGEztxdDtxFaG64Y5xzi8PMI9WIi/FxxRnH88fyUJ+OLVxP8PVrPU4lIiIidRHuCgtHOedsb1uV\n4yZV7Fu1x/gtzrkLnXMZzrnkis+bU8ffRaoxpHMrUof/ivcDBwHgm/8cLHzZ41QiIiISrkhO0iuN\n1G+O78UjLX/DVpcMQPnLV8D2jR6nEhERkXCovDUD8TF+bpxwDLcGfglATHEegZmXafJeERGRKKTy\n1kz0y06j57GTeDVwKAD+pW/C3Ke9DSUiIiK1pvLWjFw0shvT217JRpcOQOC1ayF3mcepREREpDZU\n3pqRGL+PW88awY3BiwDwlxcSeO48KNvfimQiIiLSWKi8NTPd2rRgxOif83D5KQD4N+XgNH2IiIhI\n1FB5a4bOPbQz83pN4YtgbwDsmydg3nMepxIREZGaUHlrhsyMP58xhDuSrmGzSwEg8MoVsElzJIuI\niDR2Km/NVFpiLH+YeDy/DUwh6Ax/+Q7Knz0PSgu9jiYiIiL7oPLWjA3okMYxJ5/N3wJjAYjZ/B3u\ntas1/5uIiEgjpvLWzE0c1ollB1zK7EBfAGzeNPjiUY9TiYiIyN6ovDVzZsYd4wdxT8q1rHetAAi+\neT2seN/bYCIiIlItlTehRXwMt597LJcFf0uxi8XnAgSeOx82L/c6moiIiOxB5U0AOCArlV+eOY5r\nyyom8C3ZSuCZCVCc73EyERERqUrlTSqNHpBF16PP58HyMQD4Ny8hOOMCCAY8TiYiIiI7qbzJbq44\ntic5vS/n7cBgAHzL3oZ3p3qcSkRERHZSeZPd+HzG3WcN4u+tr2dxsENo5yd/hTn/9jaYiIiIACpv\nUo2kuBjuP38E18T8rnIFBvfa1bDoVY+TiYiIiMqbVKtDyyR+f95oLgpcS5GLx1yQ4IxfwupPvY4m\nIiLSrKm8yV4N7dKK884YzyVlV1DufPgCJQSfOQs2LvI6moiISLOl8ib7NOagbEaM/jnXlU0GwFeS\nT+DJcZD/g8fJREREmieVN9mvC47oSsYRk/hz2QQA/NvWEXziNCjK8ziZiIhI86PyJjVy3Ql92DDg\nYh4rHwWAb/MSgk+eBju2epxMRESkeVF5kxrx+Yw7xx/ErC6/YWbg8NC+9XNxT58BJds8TiciItJ8\nqLxJjcXF+Hjo3KE8nnkdbwaGAmA/fIF75iwoLfI4nYiISPOg8ia10iI+hn9fcDgPtr6B9wIDAbDV\nn+CeOwfKij1OJyIi0vSpvEmtpSfF8Z8Lh3NX+k18FOgPgC1/D/f8+VBe6nE6ERGRpi3s8mZmLcxs\nqpm9aWZ5ZubMbFINx06qOL66rV24maThtG4Rz+O/GsEfU27i82AfAGzJm7jp5+oMnIiISD2qy5m3\nDOBm4ABgXpifcTNw7h6bHl+MEpmpCfx78lHclHQzXwd7ABUFbtoE3QMnIiJST+pS3tYDWc65zsA1\nYX7GG865p/bYdNomimSnJ/LvyUfz2/hbd52BWzEL9/TpegpVRESkHoRd3pxzJc65H+sawMxSzMxf\n188R73RslcRjFx/D7xJv5cPAAABs9WzcE2NhxxaP04mIiDQtXj+wMAsoAIrM7GUz6+lxHglT59bJ\nPPnro7g95fe8ExgEgK2dg/vPKVCY63E6ERGRpsOr8lYE/Ae4FDgN+D/gWGC2mXXc10AzyzSzflU3\noHt9B5b9a5+eyFMXH8k96TfxamAYALZhPu5fo2DLKm/DiYiINBGelDfn3HTn3C+cc084515yzv0e\nGAW0Bm7cz/BLgAV7bDPrNbDUWGZqAk9fPJKHW9/AC4ERAFjeMoL/PB7Wf+txOhERkejn9WXTSs65\nj4HPgeP2c+hDQP89tlPrN53URqvkOJ6+aDhPt7uOh8tPBsBXuIHgYyfCive9DSciIhLlGk15q7AG\naLWvA5xzG51zOVU3YHnDxJOaSkuM5alfHcZn3a/gD2XnAuAr3Y57ajzMn+FxOhERkejV2MpbN2CT\n1yEkMpLiYnj0vIMpGPgrppReRomLwYJl8MIF8Mn94JzXEUVERKJOvZc3M8sysz5mFltlX5tqjhsN\nDAHerO9M0nBi/T7uGn8gnY+cyKSy6yhwiaE33v49vHyZltMSERGppZi6DDazKUA6kF2x6xQz61Dx\n+m/OuXzgDuB8oCuwquK92Wb2DTAHyAcGA78kdNn0T3XJJI2PmXHNqD48kZrAWa+k8M/Yu2hvm+Gb\nJ0NPoZ75BCTt82q5iIiIVKhTeQN+C3Su8vO4ig3gKULFrDrPAScBxwNJhFZreBSY6pzbUMdM0kid\nd1gX2rQYy5nPpfOg7y4G+pbDqo9w/zwO+/l0yOjhdUQREZFGr06XTZ1zXZxztpdtVcUxk6r+XLHv\nJufcIOdcunMuzjnX2Tl3iYpb03figCweuuhEpsTdxquBQwGwvOW4R4/Rk6giIiI10NgeWJBm4KCO\n6Uyfcgx/b30Dfy0Pnai1knzck6fpQQYREZH9UHkTT2SnJzL918NZ0OtSLi+9lGIXi7lg6EGGGb+E\n0kKvI4qIiDRKKm/imeT4GP4xcQhZI87l9NJb+cFlhN7IeRH3z+Ngs6bvExER2ZPKm3jK5zN+d+IB\nTD7rNMYH7+CjQH8AbONC3CNHwWLNHCMiIlKVyps0CqcObM9jl5zA71tM5e/lpwBgJQUw7Sx4+2YI\nlHmcUEREpHFQeZNG44CsVGZediSfd7+ci0uvZLtLCL3xyV/hPydB/g/eBhQREWkEVN6kUUlLiuVf\n5w+l11E/5+TSP7IwWDGN4JrPcQ8focuoIiLS7Km8SaPj9xlXHd+bWyaN4Rf+P/Fk+XEA2I4tocuo\nb90I5SUepxQREfGGyps0Wkf3zuSlK4/l5Q5XM6X0MrbtXBf10wfgn8fCxu+8DSgiIuIBlTdp1LLS\nEpn2q0PpNHIip5Tezvxgl9AbP87HPXIkfP6IJvUVEZFmReVNGr0Yv49rT+jD1F+cyoUxd/Bg+RiC\nzrDyYnjjGnh6PGzTymoiItI8qLxJ1DiyVxte+c0xzOl+GRNKb9o1qe+yd3APHQoLXvQ2oIiISANQ\neZOokpmSwL8nDeWUMeMZG7iTlwKHA2A78mDGL2D6eVCY63FKERGR+qPyJlHHzDj3sC48e/koHm1z\nA1NKLyPPtQi9uXAm7sFhkPOStyFFRETqicqbRK0emSn895LhdBo5kRNK7+LNwFAArCgXnj8fnp8E\n2zd5G1JERCTCVN4kqsXFhB5meOSS0dydfhOXl17Klp1n4XL+i3vgYPjmKT2RKiIiTYbKmzQJAzum\n8+rlI8gecR6jSv+PtwIHA2DFW2HmpfD4KbB5uccpRURE6k7lTZqMhFg/15/Yh3/8ejR3tbyZi0qv\nZINLD7256iPcQ4fBh3dDeam3QUVEROpA5U2anEGdWvLa5UfQ5+hzOKH8nl3LawVK4L3b4OEjYOWH\nHqcUEREJj8qbNEnxMX5+87NeTL98FDPbX83pJbewNNg+9Gbu4tBl1BkXwLYfvQ0qIiJSSypv0qT1\nbJvC9IsO47RTT+dMu4s/l02gyMWH3lwwA/e3g+HThyBQ7m1QERGRGlJ5kybP5zMmHtqZN68+lnX9\nL+a4krt4Y+e0IqXb4K3fhS6lLn/P46QiIiL7p/ImzUbb1ATuP3sQd194Enen38T5pdexKtg29Oam\nRfDkafDMBD2VKiIijZrKmzQ7h/fI4I0rRnLYqLM41d3NnWUT2O4SQm8ueSO0QsNbN8KOrd4GFRER\nqYbKmzRLcTE+Lj6yO29c/TPW9r+Yo0vuYXr5kQSdYcEy+PQB3P2D4LO/a2oRERFpVFTepFnLTk/k\n/rMH8fCvR/NUu2sZU3obXwR7AxWL3b95PTx4COT8V6s0iIhIo6DyJgIM6dyKly4Zzi/Gn8aUuD9y\ncemVrNx5P9yWlaF1Uv/1M1g929OcIiIiYZc3M2thZlPN7E0zyzMzZ2aTajE+3cweMbNNZlZoZrPM\nbHC4eUTqyuczTh/SgVnXHE3vo8/hVHcvt5SdT97OtVJ/+BIeOxGeGg/r5nobVkREmq26nHnLAG4G\nDgDm1WagmfmA14CfAw8A1wKZwPtm1rMOmUTqLDk+ht/8rBdvX3McJYMv5OjSv/BQ+RiKXWzogGVv\nwyNHwvTzYdMSb8OKiEizU5fyth7Ics51Bq6p5djxwOHAJOfcVOfcg8BRQACYWodMIhHTNjWBP59+\nIM9feQJzelzOyJK/8GT5cZQ5f+iAhS/hHhoGL10CeSu8DSsiIs1G2OXNOVfinAt3baHxwAbgxSqf\ntwmYDpxqZvHh5hKJtF5tU/j3pKH8bfKJzGx/NceU3s0LgSNCT6a6IMx9OrRSw8xLIW+l13FFRKSJ\n8+qBhUHA18654B77vwCSgF4NH0lk34Z1a83zFx/GH84/mX9lXM8JpX/etVKDC8A3T+EeOBhmToEt\nqz1OKyIiTVWMR9+bBXxYzf71FX9mA/OrG2hmmUCbPXZ3j1w0kb0zM47uk8mRvdrw+oLu3PV2H/6W\nm8MVMS8yyj8HC5bDN0/i5k3DBpwJI66CDN3GKSIikeNVeUsESqrZX1zl/b25BLgl4olEasHnM04+\nMJsT+2fx8rwe/Pndfty/OYcrY17gZ/6vQiVu3jOhEtdvLIy4GtoN8Dq2iIg0AV6Vtx1Adfe1JVR5\nf28eAp7fY193YGYEconUit9nnDaoA6ccmM3L83rwx3f78pe8HC6NmckJvi/xmQtN8JvzX+g5Co64\nEjodBmZeRxcRkSjlVXlbT+jS6Z527lu3t4HOuY3Axqr7TP8iFI/F+H2MG9yBMQdlM3NuT+55vz/3\n5i7mkpiXGeObTYwFYelboa3DUBh+BfQ+CXyaJ1tERGrHq/I2FxhhZr49HloYBhQBmjxLolKM38fp\nQzowdlB73srpzQPv9eW+H5dysf9Vxvs/JN7KQpP9PjcRWveAw6bAQRMgdl93CoiIiOxS7//Zb2ZZ\nZtbHzGKr7J4BtAXGVTkuAzgDeMU5V939cCJRw+8zRg/I4rXLj+AP55/MC9lXM7zkfv5WPpZ8lxQ6\naPMyePVKuK8fzPoTbN+47w8VERGhjmfezGwKkE7o6VCAU8ysQ8Xrvznn8oE7gPOBrsCqivdmAJ8B\nj5lZXyCX0IMIfvQwgjQhO59OPbpPJnNWHcDDH/Ti8EWncJb/fS6IeZ32thmKNsMHd+I+vi/0hOph\nl0Dbfl5HFxGRRqqul01/C3Su8vM4dp1NewrIr26Qcy5gZqOBu4DLCT1d+iWhFRcW1zGTSKN0cJdW\n/LNLK5Zt7M0jH/bkuG+O5zj3ORfEvM5A3wosUApznwptXUbAsIug14ng9+ruBhERaYzMOed1hjoz\ns37AggULFtCvn85YSHTYUFDME5+u4unPVtO9OIcLY17neN8c/Fblf5NpHWHoBTD4fEhq5VlWERGJ\nnJycHPr37w/Q3zmXU9vxKm8iHttRGuCFr3/g35+spCx3Bef53+Ys//ukWlHlMS4mAes3DoZeCO0H\na6oREZEopvKGyps0DcGg4/0lG3nsk1XMWfoDp/k/4Xz/W/T2/bD7gVkHwcEXwIDxEJfsTVgREQmb\nyhsqb9L0LN+0nSdmr2LGV2s4sHw+E/1vc7zvK2ItUHmMi0/FDjwLhpyv1RtERKKIyhsqb9J0bSsu\n44WvfuDJz1azbdMPTPDP4uyY98iyvN0PzB4cKnH9T4f4FG/CiohIjai8ofImTZ9zjs9W5PHUZ6t5\nJ2ctR/IVP/e/x0jft6EluHYeF5uM9T8NBp0LHYfp3jgRkUaoruVNcxCIRAEz47DurTmse2s2FvTl\nuS/7cMMXI7D8NZwZ8z5n+j8gy/KwskL45qnQ1roHDDwHDjobUqtbjU5ERKKRzryJRKlA0PHR0k08\n+8UaZi1ax3DmMsE/i6N9c3e/N858WPdjQ8tw9TlJS3GJiHhMZ95Emim/zziqdyZH9c5k47Z+vPBV\nX+6YM4Lf5a5jrP9jzvR/QG/fD5gLwrK3Q1tcCvQ7FQ6cAJ2Hg6/eV8gTEZEI05k3kSbEOcec1Vt4\nfs4aXvt2Hd3LlnKG/wNO8X9KuhXufnBqBxhwOgw4E9r19yawiEgzpAcWUHkTqU5hSTlvLPiR5+es\n4ZuVGzjaN5dx/o842vcNcVUuqwKQ2Tc0b1z/8dCyc/UfKCIiEaHyhsqbyP6sySti5ty1vPj1Wrbk\nrudk/2ec6p/Nwb4lPz24/cHQfxz0Ow1Ssxs+rIhIE6fyhsqbSE0555j3Qz4vfv0Dr367nqSiHxjj\nm81Y/yf08q3d/VgM63RYqMgdcAqktPMotYhI06LyhsqbSDjKAkE+WZbLzLnreCtnPZ3LVnKy/1NO\n9n1GZ9/G3Y4NFblDoe+poSKX1sGj1CIi0U/lDZU3kbraURrg7UUbeHXeOt5fspHegeWhIuf/jPa2\n+acDOgyFPieHilzr7g0fWEQkiqm8ofImEkkFxWW8nbOBV79dx8dLN9LPLedE/+eM9n1BR9+mnw7I\n7LuryLUboFUdRET2Q+UNlTeR+rK1qJS3F27gzQU/8tHSTfQKLme0/wtO8H1BN9+PPx2Q1hF6nwi9\nR4fmkYuJa/jQIiKNnMobKm8iDaGguIz3Fm3k9fnr+WDJRjoF1jDK9yUn+L+kv2/VTwfEp0KP40Jl\nrsdxkNSqwTOLiDRGWmFBRBpEakIsYwe1Z+yg9hSVlvPhkk28ueAQzv5uI2kl6znO9xU/833FMN8i\nYiwIJQWQ8yLkvBhaoqvjMOg1CnqOgswDdHlVRCRMOvMmInVSWh7ksxWbeXvhBt5ZtIHC/FyO8n3L\ncf6vOMo3l1Tb8dNBaR2h58+gx8+g60iIb9HwwUVEPKLLpqi8iTQWzjly1hVUFrnF6/IY6lvM0b65\nHOv7mu6+9T8d44/DOh8eurTa4zho00dn5USkSVN5Q+VNpLFat3UHsxZv5L1FG/l4WS5ZgbUc45vL\nUb65DPMtIt7KfzooJRt6HAPdj4VuR+leORFpclTeUHkTiQbFZQE+Xb6Zd7/bwPuLN7F5yxYO8y3k\naN9cjvLNq3YaEodh2QNDJa7b0dBxGMQmNHh2EZFIUnlD5U0k2jjnWL6pkPcXb+T9xZv4YuVm2gfX\nMdL3LUf65nGobxFJVvLTgTGJ0OlQ6HYkdD0Ssg4Cn7/hfwERkTrQ06YiEnXMjB6ZLeiR2YILR3Sj\nqLScz1Zs5oPFh3Lb0lzW5m7lYN9ijvAt4AjffPrbKnzmoHwHrJgV2gAS0qHLEaEi13WE7pcTkWZB\n5U1EPJcUF8MxfdpyTJ+2AKzJK+KDJYP4ZFku/1i+GduRx+G+HI7wzWe4L2fX2qvFW+G7V0MbQFIG\ndBkOXUaEJglu0wd8Po9+KxGR+qHLpiLSqAWCjgVr8/l4WS4fL83lq++3kBnYwGG+HIb7FjDcl0Mb\ny69+cGIr6Hz4rq3tAPDrv1lFxFu65w2VN5HmpLgswFert/DJslxmL9/Mtz9soSvrONS3iEN9CznU\nt2jvZS4uBToeAp0OC907134IxCU17C8gIs2eZ/e8mVk88AfgXKAl8C1wk3Pu7f2MmwQ8tpe3s5xz\n1SyYKCISkhDrZ3iPDIb3yABCy3bNWZXHZytG8OiKzVyxditdWccw33cc4lvEMN93ZFleaHDpNlj+\nbmgD8MVC9sDQU6w7t5S2Hv1mIiI1U5frB/8BxgN/AZYCk4DXzexo59zHNRh/M7Byj31b65BHRJqh\n1ITY3e6XKygu48uVeXyxcgT/WZnH1Wu3ku02hMqcfcfBvsV081X8N2KwDH74MrR9+kBoX3rniiJ3\nCHQYCm3761KriDQqYf0/kpkdAkwArnHO3V2x7wlgAfB/wOE1+Jg3nHNzwvl+EZG9SU2I5dgD2nLs\nAaEyV1hSzjffb+WLlcN5YWUev1+zlZTSLQzxLWaobzEH+xbTz1YTa4HQB2xdHdrmTw/9HJsE2YOh\nw8GhMtd+CKRmefTbiYiEf+ZtPBAAHtm5wzlXbGb/Av5kZh2dc2v29yFmlgIUOecCYeYQEdmn5PgY\njuiZwRE9Q5dZS8uDLFiXz5xVw/h81RYeWr2FosJtHGgrONi3hMG+JQzxLaWlbQ99QFkRrP44tO2U\n2j5U4nZu2QMhPsWD305EmqNwy9sgYIlzrmCP/V9U/DkQ2F95mwW0AErN7C3gaufc0jDziIjUSFyM\nj8GdWjK4U0smjwxNGLxqcxFfrd7CV6u3cNfqLSzZWEAXfmSwLWWwbymDfMvobd/jt4oHvArWhrZF\nLwMVK0G06R06Q9d+cOjPtv20GoSI1Itwy1sW8NMVpnfty97H2CJC98vNAgqAIcBVwGwzG7y/M3Zm\nlgm02WN39xpkFhH5CTOja0YyXTOSGT+kAwD5O8qYt2Yr33y/lf99v4U7v99CefF2BthKBvqWMdC3\njIN8y8mueBDCcLDpu9A275nQB/tiIPMAyBoI2YNCZ+cyVehEpO7CLW+JQDVr11Bc5f1qOeemA9Or\n7Hqp4szbh8CNwMX7+e5LgFtqHlVEpHbSEmMZ2asNI3uF/jsxGHSsyC1k3pqtzPthK39fs/X/27vz\nIDnO8o7j32f2nL212tW1OlaWJcuWANsYOzghiYsrAVcZgp0QUgEbBxMowpECKmUqUIQULodUQoxD\nKBIHEiCAbZxgwlUVjHGIiQzELluSbVmXde19zH3uvPnj7dGORrOndmd3tb9P1Vvd+/bbM+/MUzP7\nTPf7dvNsX5Q1E6O8LHSEl4aO8jLzyw5L+Acp5KH/GV+e/Iqvs5ogoXuZLxteChv26pSriMzJfJO3\nFNBQob6xZPusOed+amb7gNfMovnngQfK6nYA357Lc4qIzFYoNHk7r7cER+fSuQkO9kV5+uQ4T5+K\n8B+nxjk6HGcrA7zUjrI3dIyX2DH2ho7RZsFXopuAgf2+PPW1ySdYsx02vCRI5l7iT7m2b9atvkSk\novkmb31AT4X64hSsM/N4zJPAZTM1cs4NAoOldaYvOBGpssa6mrNj54pi6RzPnI7wzKkIz5yO8PXT\nEV4cidNrA+yx4+wNHWePHWNv6PjkhAiAsWO+BGPo/BN0+MuUrN/jj86t3wPdl+uiwiIy7+TtKeAG\nM2srm7RwXcn2uboEGJpnf0REllxrYx3X7+ji+h1dZ+siqRwHTkc4cCbK/jMRHjgd4ehwnE1umCtC\nL3KFvcgVoRfZEzrOZhuefLD0+HmzXB2GdV7iE7n1e/wp2HVX+CN3uhadyKox30/7g8CHgTuA4nXe\nGoDbgH3FSQdmthFoB44453JBXbdz7pwkzczegJ+4cM88+yMisiy1h+u4/tIurr90MqFLZPI82xfl\nYF+Ug2eiPNoX5bn+GA35KFeETrDbTnC5nWB36ASX2UkaLQcEEyNGj/hSepSuph66dvlkrnv3ZOnc\nDqGaar9kEVlk80renHP7zOwB4K5g9udh4B1AL3B7SdO7gvrtwPGg7nEzexL4BRABrgbeiT9t+un5\n9EdEZCVpbqjlmt5OruntPFuXnyhwbDjBwb4oz/bF+H5/lL/pizIcTdJr/ey2k1wWOsFuO8luO8HW\nUMlv4Ins5Fi6UjUN0LUTui+Drsuge1eQ1O2A2voqvVoRWWgXcpz97cCnOPfepjc65x6bYb9vAm8E\nXgc04cfP/SPwSefcwAX0R0RkxaqtCbFzfSs717dy05WT9aOJLM/1R3m+P8ZzfTF+NBDjUH+MUDrO\npXaaXaFT7LJTXGYn2Rk6PXkfV4CJTMWkzlkN1rndH63r2glrd06uN3UiIsubOeeWug8XzMz2APv3\n79/Pnj17lro7IiKLqlBwnBhNcmggxguDcQ4NxDg0EOfIUJzGfIxL7TQ7Q6fZaT6x2xE6Q4+NzO7B\nw51BQndpSdnhx9VpsoTIgjhw4AB79+4F2OucOzDX/TXCVURkhQmFjN6uZnq7mnldye/V/ESBk2Mp\nDg3EODwYZ/9AjIcG4hwdjlOTS3CJ9bHTTnFp6Aw7zJdtNjB5X1eA1Cic3OdLubYe6LzEl7U7JteV\n2IlUlZI3EZGLRG1N6OzdIl5fktQVCo7T4ykOD8U5Mhjn8GCcHw/FOTqUIJJIstUG2WFnuMT62G59\nbPChHlkAABFFSURBVA/1c4mdodvK7oBYvC3Y8f8+/8lbN/okrnO7X67pDdZ7oWmtrlknsoCUvImI\nXORCIWNLZxNbOpu44bJ152wbS2Q5OhznyGCCo8MJHhmKc2w4wYsjSRonYvRaP73Wz3brpzfkl9ut\nj3ZLnvsksT5fTjx+fgfqmmHNNp/IdWzz6x3boGOrX9cdJkTmRMmbiMgqtqa5npc3d/LybedOVJgo\nOM6Mpzg6nOD4cILjIwkeDpK6E6NJmgs+sdtmA/TaANtC/WyzQbbaIOts/NwnySVg8KAvlYQ7fSJX\nWtq3QMcWv2xs15E7kRJK3kRE5Dw1JUfrfiO4x2tRfqJAXyTN8ZEEx0eSnBhJ8IORJCdHk7w4koRc\ngq02yDYbYIsNsdmG2BIkdltsiLBlz32y1KgvfVNc372+NUjkNvvS1uOTuvYev97Wo0ufyKqi5E1E\nROaktiZ0NrF71c5ztznnGI5nOTGa4MRokpOjKQ6OJvnhmF/viyTpdFG2BIncZhtisw2y2YbpsWE2\n2zANwUWJz8rGpj9yB9C8Dto2BcndJl9aN5Wsb9SkCrloKHkTEZEFY2Z0tzbQ3dpw3qlYgNxEgb7x\nNKfGkpwcS3JqLMUTo0n+fTzFqbEUg1Gf3PUEydymYNljI/TYMBtt5Nz7whYlBn2Z6ugd+PvFtm6E\n1g0VlkFpWQ+1DQv4jogsPCVvIiJSNXU1IbaubWLr2spHwXITBfojaU6NpTgz7svzkRSPjKc5PZak\nL5LGZRJsshE22QgbbYQeG2E9o2y0UTbaCBtslDZLnf/g6XFfhp6dvpPhziCRWwctxeX6oKybXIbX\naCyeLAklbyIismzUlZySrcQ5RzSV50wkRV8kRV8kTd94mp9H0vRHJ/8O5eJssFFfGGO9+eRunY2z\n3sZYb2N0M06tFc5/kuIYvOlO0wIuVIc1d0NLtz9t27IOmrv8enM3NK+Fpq5gvUtH9GTBKHkTEZEV\nw8xob6qjvamOyze2VWzjnCOazjMQTdMfSdMfTTMQSfNcNM1jsQyD0TSDsQzDsRQdhQjrbJx1QUK3\nDp/cdds43RbxS8ZpsPz5fSnkIHbGl1lw9a3Y2YSuyy+bOv118M4rnf40byh0Qe+XXJyUvImIyEXF\nzGgP19EermPX+qmvIVcoOEYSWQZjPpkbimYYjKV5Ppbhf+IZhmLFkqY2E6XLfKLXTYSuILHrCta7\nLMJai9JFhPrSO1aU9isb85Mvxo7P6nU4DMJrsKZOfyq3qdOfqg13QtOaYL1CqW9V0neRU/ImIiKr\nUig0ObliprtiJzJ5huMZhotJXTzLcCzDU/EMI/EsI4kMw/Esw7E0lo6y1qJ0EqXLJtfXWpROiwXr\nMTotyhpiFY/qARhu8hTuHDgLQWM7Fl7jj96FO/y18hqDZfHvhrbJusa2oK4V6po0lm+ZU/ImIiIy\ng+aGWpobatm2tnnGtpn8BGOJHCOJDKOJLKOJLCPxLKPJLC8ksowlsowE9eOJDJlUnHbnE7m1FqWD\nOJ0Wo8NidBKjw+KsIc4ai9MerDdZZsrnN1eA1Jgv8+BCtf4Ub2Mb1tjmk7yGNp/YNQbLhtZz6xta\noL7Fr9e3+L/rmnUEcJEoeRMREVlADbU1bGivYUN746zaF8fojSeDhC6ZYyzpl4PJLM8nc4yncowH\ndeOpLKlEglAmQrsl6CBOh/nSRoIOS9BOwtcRp82StJKkLaif6rRukRXyWHoM0vNL/s6+LgxX1wT1\nzdDQijW0YPVBklffHJTiepNfL7avbw7Wm3wSWN/k/65rgrrwqj8yqORNRERkCZWO0ZvNkb2i/ESB\naDpPJJUjEiR3kVSOaDpPNJXjVCpHJJkjms4RS+eJpnNEk1kyqTiWiRJ2SdpJ0GZJ2oLkroUUrZak\n9ewySaulfD1JWixFKynqZkgAwZ/2tVzC3x4tMXghb9F5CrVhXF0Tri6M1TVjdWGsPozVhScTvNrG\nYD1Y1jaW1FdaNkBtsKwL+7GFy/TIoZI3ERGRFai2JkRncz2dzXO/NZhzjnSuQCztk73iMpHx67F0\nnhczeWLFukyeeDpPPJMnlsqSy6QhEyOUjRF2CVotRTNpmknRYmmaSNNi/u9m0rQUtwfbmsicXW8m\nTY25OfU/lE9BPgUVLue3UNIfOkxje/fMDZeAkjcREZFVxswI19cQrq9hXeUrrsyKc45MvkA8kyeZ\nmSCRzZPM5olnJkhk8iSzfnky67fHM3lSWd8ulZ0gmZ0gmckxkUvhskksm8BySUL5JE2WIUyGJjKE\nz1vP0kSasPm6RrI0kiNsGRrIEiZLo2UJB9ummhQynQx1zO7Ed/UpeRMREZF5MTMa62porKuBloV7\n3OKRwVRuwpdsnlS2QDKbJ50vkMpOkA62RbITpPMTpHMFMjlfn84VgrqgPpvF5dOQTUE+jeXT2IRf\nhiYy1BZ80tdAjkbzy4+FZ38Ku9qUvImIiMiyUnpksBoKBUd2okAmVyCTnyCTL9BQt3xTpOXbMxER\nEZEqCIWMxlBwBJG6pe7OjJbnNAoRERERqUjJm4iIiMgKouRNREREZAVR8iYiIiKygih5ExEREVlB\nlLyJiIiIrCBK3kRERERWkHknb2bWYGZ3m9kZM0uZ2T4ze+0s9+0wsy+a2ZCZJczsx2Z29Xz7IiIi\nIrJaXMiRty8Dfwp8DfgAMAF8z8x+bbqdzCwEfBd4G3Av8FFgHfCome28gP6IiIiIXPTmdYcFM7sW\neCvwEefcXwd1/wrsB/4KuH6a3W8Ott/inHsw2Pd+4BDwSXxSJyIiIiIVzPfI2834I21fLFY459LA\nfcArzWzLDPsOAA+V7DsE3A/cZGYN8+yTiIiIyEVvvsnbVcAh51y0rP6JYHnlDPv+n3OuUGHfJmDX\nPPskIiIictGb743pNwJ9FeqLdZtm2PexGfZ9ZqqdzWwd0F1WvRvg8OHD0zytiIiIyNIryVfq57P/\nfJO3MJCpUJ8u2b4Y+wK8F/hEpQ1vetObZthVREREZNnYAjw5153mm7ylgEpj0xpLti/GvgCfBx4o\nq2vBn27dD2Rn2P9C7AC+DdwEHFnE55G5UVyWJ8Vl+VJslifFZfla6NjU4xO3n8xn5/kmb31AT4X6\njcHyzAz7bqxQP5t9cc4NAoMVNu2bbr+FYGbF1SPOuQOL/XwyO4rL8qS4LF+KzfKkuCxfixSbOR9x\nK5rvhIWngF1m1lZWf13J9un2vTq43lv5vkn8JUNEREREpIL5Jm8PAjXAHcWK4BIftwH7nHMng7qN\nZrbbzOrK9l0P/E7Jvl3ALcB3nHOVxsOJiIiICPM8beqc22dmDwB3BbM/DwPvAHqB20ua3hXUbweO\nB3UPAv8LfMnMrgCG8ZMQaphiIoKIiIiIePMd8wbwduBTwB8Ca4CngRudc5UuA3KWc27CzN4AfAZ4\nP3526c+BW51zz19Af6phCH8XiKGl7oicQ3FZnhSX5UuxWZ4Ul+VrWcXGnHNL3QcRERERmaULuTG9\niIiIiFSZkjcRERGRFUTJm4iIiMgKouRNREREZAVR8iYiIiKygih5ExEREVlBlLzNwMwazOxuMztj\nZikz22dmr13qfq0mZvYKM7vXzA6YWcLMTpjZ/Wa2q0Lby83sB2YWN7NRM/uKmXUvRb9XIzP7mJk5\nM9tfYZtiU0VmdrWZPRy810kz229m7y9ro5hUmZntNLNvmNmpIC7PmdnHzayprJ1is0jMrMXMPhm8\nv6PBd9atU7SddRzM7HYze9bM0mb2gpn9yaK9Bl3nbXpm9nXgZuCzwAvArcArgBuccz9dwq6tGmb2\nIPCrwAP4i0FvAN4HtAC/4pzbH7TbjL/RbwS4J9j+YeAEcK1zLlv93q8ewfv/POCA4865vWXbFJsq\nMbPXAd/Bv+ffBOLADiDknPto0EYxqTIz24L/DosAXwBGgVfi/6887Jy7KWin2CwiM+sFjuHfz6PA\nbwK3Oee+XNZu1nEws3fjY/ot4IfAq/A3Mfgz59zdC/4inHMqUxTgWvw/og+X1DXibwf2+FL3b7UU\n4HqgvqxuJ5AGvlpS93kgCWwtqXtNEMM7lvp1XOwF+AbwI+BRYH/ZNsWmenFoA/qBh/DJ2lTtFJPq\nx+bO4P3dU1b/L0H9GsWmKnFoADYE69cE7+utFdrNKg74O0UNA/9Ztv9X8T+c1iz0a9Bp0+ndDEwA\nXyxWOOfSwH3AK4NfUbLInHOPu7Jfms65F4ADwOUl1W/Bf3hOlLT7L+AQ8LvV6OtqZWa/jv+8fHCK\nJopN9bwNWA98zDlXMLNmM6v0Xa+YVF9bsBwoq+8DCkDxe06xWUTOuYxzrn8WTWcbhxuAtfhkr9Tf\nA83AGy+sx+dT8ja9q4BDzrloWf0TwfLKKvdHAmZm+H9Qw8HfPcA64BcVmj+Bj6UsAjOrAT4H/JNz\n7pkK2xWb6noNEAV6zOx5/C//qJn9g5k1gmKyhB4NlveZ2ZVmtsXMfg94D3CPcy6h2CwPc4xDcb28\n7S/xSfmCx0zJ2/Q24n8RlSvWbapiX+RcfwD04MfzgI8VTB2vTjNrqEbHVqE/BrYBfz7FdsWmunYC\ntcC38WNv3gL8Mz5OXwraKCZLwDn3A/zn5LX4sVQn8MMNPuec+1DQTLFZHuYSh43AhHNusLRRcMZo\nhEXIFWoX+gEvMmEgU6E+XbJdqszMduMPR/8MP1YEJmMxU7wqbZd5MrO1wF8An3LODU3RTLGprhag\nCfiCc644u/QhM6sH3m1mH0cxWUrHgcfwA9tH8KfU7jSzfufcvSg2y8Vc4hBm8pR3pbYLnisoeZte\nCj+wsVxjyXapIjPbAHwXP/vnZufcRLCpGAvFq7r+Ej9j7nPTtFFsqqv4Xn69rP7fgHfjZzceDOoU\nkyoys7fix1Dvcs6dCqofCsYk3h1c3UCfl+VhLnFIAfVTPE4jixAvnTadXh+Th05LFevOVLEvq56Z\ntQPfBzqA33LOlb7/xUPbU8Vr1DmnX6oLyMx2Anfgp9BvMrPeYAp+I1AX/N2JYlNtxc9F+aD44imd\nNSgmS+W9wJMliVvRw/ijpVeh2CwXc4lDH1BjZutKGwVHu9eyCLmCkrfpPQXsMrO2svrrSrZLFQQD\nrb8D7AJudM4dLN3unDsNDOGnfZe7FsVqMfTgv0PuwV8zqViuw8fpGPBxxabqfhkse8rqi+NuhhST\nJbMeqKlQXxcsaxWb5WGOcSiul7e9Bv8dueAxU/I2vQfxH7Q7ihXBAMXbgH3OuZNL1bHVJJjN+E38\n6Z5bnHM/m6Lpt4AbSy/hYmavxicSDyx6R1ef/cCbK5QD+IHYb8ZfVgcUm2q6P1jeXlb/R0CeyRmP\nikn1HQKuqnB3mN/Hz0p8OvhbsVkeZhuHR/DDR95Ttv978NeJ++5Cd0x3WJiBmd2P/yf0t/iL874D\nn3W/2jn32FL2bbUws88CH8Afebu/fLtz7qtBuy34GVzjwN/hB25/BDgFvEKnGqrDzB4Futy5d1hQ\nbKrIzO4D3on/vPwEfwX5W4C7nHN3Bm0UkyoLrof4CH6iwr3B8kbgt/GX2nlX0E6xWWRm9j78EJxN\n+CTrIfx7Dn72b2QucTCz9+In0j3I5B0W3o6/3uKnF/wFLPWVjpd7wY/f+Qz+nHYaf32X1y91v1ZT\nwR8pcFOVsrZ7gg9OAhjDX+F6/VK/htVUqHCHBcWm6jGoAz6Bn9mYxd/a74OKydIX/I//7wX/U7L4\nW8rdiT9lqthULw7Hp/m/0jufOADvAp7Dz0A9jL9ouS1G/3XkTURERGQF0Zg3ERERkRVEyZuIiIjI\nCqLkTURERGQFUfImIiIisoIoeRMRERFZQZS8iYiIiKwgSt5EREREVhAlbyIiIiIriJI3ERERkRVE\nyZuIiIjICqLkTURERGQFUfImIiIisoIoeRMRERFZQZS8iYiIiKwg/w+Kfy2z1JEocAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b60350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.201746\n",
      "Epoch 96, train loss: 0.201590\n",
      "Epoch 97, train loss: 0.199461\n",
      "Epoch 98, train loss: 0.197421\n",
      "Epoch 99, train loss: 0.195498\n"
     ]
    },
    {
     "data": {
      "image/png": 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JGQBaIktERERqlMpblPYnRspb4/COb00MKCIiIvVXdfcClbcohZIzAcggj7y9BzxOIyIi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\nRDxI6uKREJ+97y5yyZUfhawlJW8iIiL1IhRaGHX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11446c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.178193\n",
      "Epoch 96, train loss: 0.196805\n",
      "Epoch 97, train loss: 0.194629\n",
      "Epoch 98, train loss: 0.192482\n",
      "Epoch 99, train loss: 0.190477\n"
     ]
    },
    {
     "data": {
      "image/png": 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3TR8KME+nUDVkMixfTAIVrFv0MkMu+LrXkURERKSDCbS8jaz/9Yr6x7GebWLs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\neVkRERGRtVeRrzStpP1Kk7dmIFulPFOxfzXaAnw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114553f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.206452\n",
      "5-fold validation: Avg train loss: 0.193376, Avg test loss: 0.197332\n"
     ]
    }
   ],
   "source": [
    "train_loss, test_loss = k_fold_cross_valid(k, epochs, verbose_epoch, X_train,\n",
    "                                           y_train, learning_rate, weight_decay)\n",
    "print(\"%d-fold validation: Avg train loss: %f, Avg test loss: %f\" %\n",
    "      (k, train_loss, test_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fine-tuning, even though the training loss can be very low, but the validation loss for the $k$-fold cross validation can be very high. Thus, when the training loss is very low, we need to observe whether the validation loss is reduced at the same time and watch out for overfitting. We often rely on $k$-fold cross-validation to fine-tune parameters.\n",
    "\n",
    "## Make predictions and submit results on Kaggle\n",
    "\n",
    "Let us define the prediction function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,\n",
    "          weight_decay):\n",
    "    net = get_net()\n",
    "    train(net, X_train, y_train, None, None, epochs, verbose_epoch, \n",
    "          learning_rate, weight_decay)\n",
    "    preds = net(X_test).asnumpy()\n",
    "    test['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
    "    submission = pd.concat([test['Id'], test['SalePrice']], axis=1)\n",
    "    submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fine-tuning parameters, we can predict and submit results on Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96, train loss: 0.171177\n",
      "Epoch 97, train loss: 0.170568\n",
      "Epoch 98, train loss: 0.170056\n",
      "Epoch 99, train loss: 0.169555\n"
     ]
    },
    {
     "data": {
      "image/png": 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XIiIiIgNI4a2PJo2MCW/7FN5EREQksRTe+igrLYVRud4B9ep5ExERkURTeOsHHStONedN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\nOsh/Ab/LADROBoeLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145b3f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,\n",
    "      weight_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After executing the above code, a `submission.csv` file will be generated.  It is in the required format by Kaggle. Now, we can submit our predicted sales prices of houses based on the testing data set and compare them with the true prices on Kaggle. You need to log in Kaggle, open the [link to the house prediction problem]((https://www.kaggle.com/c/house-prices-advanced-regression-techniques)), and click the `Submit Predictions` button.\n",
    "\n",
    "\n",
    "![](../img/kaggle_submit.png)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "You may click the `Upload Submission File` button to select your results. Then click `Make Submission` at the bottom to view your results.\n",
    "\n",
    "![](../img/kaggle_submit2.png)\n",
    "\n",
    "Just a kind reminder again, **Kaggle limits the number of daily submissions to 10**.\n",
    "\n",
    "\n",
    "## Homework: [We award Bitcoins/Ethers](https://www.reddit.com/r/MachineLearning/comments/70gaqs/we_award_bitcoinether_for_users_who_use/)\n",
    "\n",
    "* What loss can you obtain on Kaggle by using this tutorial?\n",
    "* By re-designing the model, fine-tuning the model, and use the $k$-fold cross-validation results, can you beat [the 0.14765 baseline](https://www.kaggle.com/zubairahmed/randomforestregressor-with-score-of-0-14765/) achieved by Random Forest regressor (a powerful model)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For whinges or inquiries, [open an issue on  GitHub.](https://github.com/zackchase/mxnet-the-straight-dope)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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